Music driven dance synthesis by multimodal dance performance analysis: Music-Driven Dance Synthesis by Multimodal Dance Performance Analysis - Couverture souple

Demir, Yasemin; Erzin, Engin; Tekalp, A. Murat

 
9783846549902: Music driven dance synthesis by multimodal dance performance analysis: Music-Driven Dance Synthesis by Multimodal Dance Performance Analysis
Couverture souple
ISBN 10 : 3846549908 ISBN 13 : 9783846549902
Editeur : LAP LAMBERT Academic Publishing, 2011

Synopsis

We present a framework for audio-visual analysis of dance performances towards the goal of music-driven dance synthesis. Dance figures, which are performed synchronously with the musical rhythm, can be analyzed through the audio spectra using spectral and chromatic musical features. In the proposed multimodal dance performance analysis system, dance figures are manually labeled over the video stream and modeled by employing HMMs. The music segments, which correspond to beat and meter boundaries, are used to train hidden Markov model (HMM) structures to learn meter related temporal audio patterns which are correlated with the dance figures. Bi-gram based co-occurences of temporal audio patterns and dance figures are calculated. and bi-gram based co-occurrence performances for two different audio feature streams are evaluated. In our evaluations, mel-scale cepstral coefficients (MFCC) with their first and second derivatives and chroma features are used as our candidate audio feature set. The proposed framework in this thesis, can be used towards analysis and synthesis of audio-driven human body animation.

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Présentation de l'éditeur

We present a framework for audio-visual analysis of dance performances towards the goal of music-driven dance synthesis. Dance figures, which are performed synchronously with the musical rhythm, can be analyzed through the audio spectra using spectral and chromatic musical features. In the proposed multimodal dance performance analysis system, dance figures are manually labeled over the video stream and modeled by employing HMMs. The music segments, which correspond to beat and meter boundaries, are used to train hidden Markov model (HMM) structures to learn meter related temporal audio patterns which are correlated with the dance figures. Bi-gram based co-occurences of temporal audio patterns and dance figures are calculated. and bi-gram based co-occurrence performances for two different audio feature streams are evaluated. In our evaluations, mel-scale cepstral coefficients (MFCC) with their first and second derivatives and chroma features are used as our candidate audio feature set. The proposed framework in this thesis, can be used towards analysis and synthesis of audio-driven human body animation.

Biographie de l'auteur

YASEMIN DEMIR was born in Turkey on May 21, 1984. She received her B.Sc.degree in Telecommunications Engineering from Istanbul Technical University, Istanbul,Turkey, in 2006. From August 2006 to July 2008, she worked as a teaching and research assistant in Koc University, Istanbul, Turkey. At Koc University, she focused on Signal Processing.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2011
Langue
anglais
ISBN 10 
3846549908
ISBN 13 
9783846549902
Reliure
Broché
Nombre de pages
64
Coordonnées du fabricant
non disponible
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